This invention relates to an image data compressing technology by which image data for a multispectral image reconstructed from a plurality of band images of an object captured in a wavelength range as divided into a plurality of bands can be efficiently compressed without impairing the quality of the image.
With the recent advances in digital image processing, it has become possible to provide a complete representation of image's color information (lightness, hue and saturation) by a multispectral image which carries spectral information for each of its pixels. To produce a multispectral image, an object is recorded in divided wavelength bands and on the basis of a multiband image consisting of the captured band images, the spectral reflectance distribution is estimated for each band image. The multiband image can reproduce the color information that cannot be fully represented by the conventional RGB color images consisting of red (R), green (G) and blue (B) images and will prove effective in the art world which needs more accurate color reproduction. In order to make the most of the capability for correct reproduction of color information, the multispectral image is desirably produced from a multiband image having as many bands as possible, say, 41 bands which are obtained by dividing a recording wavelength range of from 380 to 780 nm by 10 nm or even 81 bands by dividing the range by 5 nm.
A problem with the multispectral image having spectral information for each pixel is that it possesses spectral reflectance data for each group of the channels, say, 41 channels, into which the recording wavelength range has been divided and that compared to the conventional 3-channel RGB color images, the volume of the required image data is about 13 times as much (41/3). This means a large storage capacity and a prolonged time are required to save the image data for multispectral images. In addition, so much time is taken to transfer the image data over a communication network that great difficulty is involved in data handling.
To deal with this situation, Keusen proposed an improved technique in which spectral waveforms obtained from spectral information for each of the pixels in a multispectral image were expanded into three color matching functions as of the RGB colorimetric system and those portions of the spectral waveforms that could not be expressed by color matching functions were expanded into basis vectors by principal component analysis and those principal components which were representative of the image information for the spectral images were extracted and adopted but other principal components were discarded so that the spectral waveforms of interest were eventually expressed by a total of six to eight basis vectors including the color matching functions [Th. Keusen, Multispectral Color System with an Encoding Format Compatible with the Conventional Tristimulus Model, Journal of Imaging Science and Technology 40: 510 to 515, (1996)]. In accordance with this method, the image data for the multispectral image can be compressed by expressing the spectral waveforms in terms of six to eight basis vectors and their multispectral coefficients. In particular, if the spectral waveforms are expressed by the color matching functions of the RGB colorimetric system, their coefficients are R, G and B tristimulus values and the image can be directly sent for subsequent processing without performing any special transformations in order to provide compatibility for the conventional image processing and display apparatus which employ the tristimulus values of R, G and B pixels. This is a great benefit to the purpose of reducing the volume of data processing.
If a multispectral image consisting of 41 spectral images is expressed by eight basis vectors and their multispectral coefficients according to the method described above, the image data to be handled can be compressed to about 20% of the initial volume (8/41×100). Compared to the RGB color image, the volume of the image data for the multispectral image composed of 41 spectral images is about 13 times as much and even if it is reduced to about 30% of the initial value by the Keusen method, the volume is still as much as about 2.5 times the image data for the RGB color image (13×20/100). As a result, the aforementioned problem of the prior art still remains and the time for recording and storing the image data on recording media as well as the time for transferring them over a communication network are long enough to present difficulty in data handling.